Fingerprint sensing and matching is a reliable and widely used technique for personal identification or verification. In particular, a common approach to fingerprint identification involves scanning a sample fingerprint or an image thereof and storing the image and/or unique characteristics of the fingerprint image. The characteristics of a sample fingerprint may be compared to information for reference or enrolled fingerprints already in a database to determine proper identification of a person, such as for verification purposes.
A significant advance in the area of fingerprint sensing is disclosed in U.S. Pat. No. 5,940,526 to Setlak et al., which is assigned to the assignee of the present invention. The patent discloses an integrated circuit fingerprint sensor including an array of RF sensing electrodes to provide an accurate image of the fingerprint friction ridges and valleys. More particularly, the RF sensing permits imaging of live tissue just below the surface of the skin to reduce spoofing, for example. The entire contents of the Setlak et al. patent are incorporated herein by reference.
Traditional approaches for fingerprint matching generally rely on minutia, which are point features corresponding to ridge ends and bifurcations. However, minutia-based matchers have two significant drawbacks. First, minutia extraction is difficult in images of poor quality. Second, a minimum fingerprint area is needed to extract a reasonable number of minutiae. Thus, minutia-based matchers are unsuitable in applications where poor-quality images or small sensors are involved. Using fingerprint pattern features, instead of minutiae, for matching can mitigate both of these drawbacks. Examples of fingerprint pattern features are image pixel values, ridge orientation, and ridge frequency.
Ridge orientation information has been used in a variety of stages in fingerprint recognition. It is commonly extracted through tessellating the fingerprint image into small square blocks or cells and estimating the dominant ridge orientation within each block. The resulting map is referred to as the ridge orientation map, or simply O-map. The O-map is also commonly referred to as the direction map in the fingerprint recognition literature.
Ridge orientation in fingerprint recognition has been used in a variety of different ways, one of which is orientation-adaptive fingerprint enhancement. This is perhaps the most common use of ridge orientation information. In this type of enhancement, each block is enhanced using a filter tuned to the estimated local ridge orientation (and possible ridge frequency as well). Fingerprint enhancement is a common early step for minutia extraction. Further details on orientation-adaptive fingerprint enhancement may be found in the following references: O'Gorman et al., “An Approach to Fingerprint Filter Design,” Pattern Recognition, vol. 22, no. 1, pp. 29-38, 1989; Hong et al., “Fingerprint Image Enhancement: Algorithm and Performance Evaluation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 777-789, August 1998; and Almansa et al., “Fingerprint enhancement by shape adaptation of scale-space operators with automatic scale selection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 9, no. 12, pp. 2027-2042, December 2000.
Another common use of ridge orientation is the classification of fingerprints into Henry's classes (right loop, left loop, whorl, arch, tented arch). Such classification may be performed using approaches such as a hidden Markov model, as discussed in U.S. Pat. No. 6,118,890 to Senior. Another classification approach includes inexact graph matching, as discussed in Cappelli et al., “Fingerprint Classification by Directional Image Partitioning,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 5, pp. 402-421, May 1999. Still another classification approach involves the use of neural networks, as discussed in Halici et al., “Fingerprint Classification Through Self-Organizing Feature Maps Modified to Treat Uncertainties,” Proc. IEEE, vol. 84, no. 10, pp. 1497-1512.
Ridge orientation information has also been used for indexing, one example of which may be found in Coetzee et al., “Fingerprint recognition in low-quality Images,” Pattern Recognition, vol. 26, no. 10, pp. 1441-1460, 1993. This article discusses the use of several classification techniques for indexing. A number of training images were acquired for each fingerprint in a selected set. Orientation maps were computed for each image and used for classifier training. Three types of classifiers were used: linear, nearest neighbor, and neural net.
Certain prior art techniques have also utilized ridge pattern information to some extent in fingerprint matching. One example is disclosed in U.S. Patent Pub. No. 2003/0169910 to Reisman et al. The matcher disclosed therein relies upon minutiae along with ridge pattern information encoded in responses to a bank of Gabor filters. An example of another matching system is disclosed in U.S. Pat. No. 5,909,501 to Thebaud. This system uses a combination of ridge orientation, ridge frequency and ridge offset. Each of the above-noted matchers organize the ridge pattern information in tessellated maps.
Despite the advantages of such systems, it may be desirable in some applications to more fully exploit ridge orientation characteristics in fingerprint processing operations, particularly matching, for example, to reduce dependency on minutia-based matching.